WiFiUS: Collaborative Research: Data-Guided Resource Management for Dense Heterogeneous Networks

WiFiUS：协作研究：密集异构网络的数据引导资源管理

• 批准号: 1457060
• 负责人: Xin Liu
• 金额: $ 19.33万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457060&HistoricalAwards=false
• 关键词: WiFiUS; Collaborative; Research; Data; Guided

The emerging paradigm of dense heterogeneous wireless networks (HetNet), while essential to meeting the explosive growth of wireless traffic, poses unseen challenges to classical cellular network design principles. In traditional cellular systems, spectrum and resource management are often based on the assumption of static and regular topology/traffic patterns. Further, each cell operates with an orthogonal set of resources and requires little inter-cell coordination. However, such traditional approaches are out-dated and inefficient in dense HetNets:the proliferation of small-size cells makes the network topology and traffic characteristics highly irregular and varying; and tighter coordination across cells becomes a necessity when users traverse many small-cells frequently. Thus, there is an acute need to re-examine fundamental ways to design HetNets that can (i) quickly adapt to irregular topology and changing load patterns, and (ii) manage cell coordination at scale with minimal overhead to achieve robust and dependable application-level performance. To address this open challenge, this WiFiUS project develops new framework, architecture and algorithms for adaptive, efficient, and dependable spectrum management and resource allocation in dense heterogeneous cellular networks. First, the project team develops a new hypercell architecture that views a macro-cell and the overlapping small-cells as a single logical entity. This new architecture allows hypercells to acquire a global view of the load/channel/mobility/application patterns across multiple base-cells, thereby enabling more effective and dynamic multi-cell coordination at scale. Furthermore, the team develops a data-guided operational framework that exploits both provider-collected and user-contributed data to address the inherent complexity of dynamic multi-cell coordination. Based on historical data, this approach migrates higher-complexity computations to offline, and thus achieves high spectrum efficiency and user QoE with low-overhead. The US/Finland team brings together a wide range of wireless expertise from physical layer design to network resource management. Having a successful collaboration record, the team carries out joint research activities in the following three aspects: 1) data collection and traffic pattern identification; 2) data-guided spectrum management and base-station cooperation; and 3) hypercell link adaptation, measurement, and resource management. The success of the project has a broad impact on the wireless communications industry by addressing multiple timely and critical challenges that arise from the exponential growth of cellular traffic. The project outcomes will also provide important insights into big-data mining and learning for cellular resource management. The results from this work will be widely disseminated through journal/conference publications, and be incorporated into undergraduate and graduate education endeavors.

密集的异质无线网络（HETNET）的新兴范式对于满足无线流量的爆炸性增长至关重要，但对经典的蜂窝网络设计原理构成了看不见的挑战。在传统的蜂窝系统中，频谱和资源管理通常基于静态和常规拓扑/交通模式的假设。此外，每个细胞都使用一组正交资源运行，几乎不需要核对间的协调。但是，这种传统方法的量很高且效率低下：小型细胞的扩散使网络拓扑和交通特征高度不规则和变化。当用户经常穿越许多小细胞时，整个细胞的协调变得必要。因此，急需重新检查设计的基本方法来设计可以（i）快速适应不规则的拓扑和不断变化的负载模式，以及（ii）以最小的开销来管理细胞协调，以实现可靠且可靠的应用程序级别的性能。为了应对这一开放挑战，这个Wifius项目开发了新的框架，体系结构和算法，用于在密集的异质蜂窝网络中进行自适应，高效且可靠的频谱管理和资源分配。首先，项目团队开发了一种新的HyperCell体系结构，该体系结构将宏电池和重叠的小单元视为一个逻辑实体。这种新的体系结构允许HyperCells跨多个基本细胞获取负载/通道/移动性/应用程序模式的全局视图，从而使更有效，动态的多单元配位在大规模上。此外，该团队开发了一个数据引导的操作框架，该框架利用了由提供商收集的和用户组成的数据来解决动态多电池协调的固有复杂性。基于历史数据，这种方法迁移了较高的复杂性计算以离线，因此在低空头顶的情况下实现了高频谱效率和用户qoe。美国/芬兰团队汇集了从物理层设计到网络资源管理的广泛无线专业知识。该团队获得了成功的合作记录，在以下三个方面进行了联合研究活动：1）数据收集和交通模式识别； 2）数据引导的频谱管理和基础站合作； 3）HyperCell链接适应，测量和资源管理。该项目的成功对无线通信行业产生了广泛的影响，通过解决及时和关键的挑战，这些挑战是由细胞流量的指数增长引起的。该项目的结果还将为大数据挖掘和学习蜂窝资源管理提供重要的见解。这项工作的结果将通过期刊/会议出版物广泛传播，并将其纳入本科和研究生教育工作中。